Big data has taken many organizations by storm. As a result, there is a need for great commitment to capitalize on the potential big data presents while tackling specific challenges associated with it. Due to the exponential growth of most organizations, it’s very challenging to handle challenges.
There are several complex data challenges for businesses that are related to volume, generation, grouping, and processing of data. Great data strategies help streamline operations, reduce release time, and adopt new strategies. Below are complex data challenges for businesses and ways to overcome them.
Data integration and preparation complexities
When handling large volumes of data, you will encounter problems with collecting and storing large amounts of different data types. The retrieval and collection process is often very challenging. Data integrity mainly depends on how regularly you update the stored data. Great access to various data sources needs to be maintained.
Quick data retrieval is highly needed when handling large volumes of data for business analytics. With great database caching solutions, you will dramatically increase the throughput and lower the retrieval latency, especially when dealing with backend databases. You can use data lake as a catch-all repository to handle data from different sources. This way, you won’t have to worry about big data integration and preparation complexities.
Managing large volumes of data
It’s very challenging to handle large volumes of data that are housed in different systems and platforms. Consolidating the large data sets that the organization is extracting from the CRM or the ERP systems and any other data source into a manageable and unified architecture can be extremely challenging.
The best way to handle challenges associated with large volumes of data is to plan for infrastructure that supports incremental changes. Remember that fixing challenges related to big data may create a recipe for more problems. Blockchain technology in business provides secure environments through the distributed ledger. Large volumes of data are stored with high resiliency and integrity.
Finding and fixing data quality issues
Even if you are using the latest tools in handling data from different sources, big data processing will generate bad results when quality issues creep in during data processing. Problems associated with finding and fixing data quality issues will become more challenging to audit as the data management team has to pull data from disparate sources.
Consequently, it will be very hard to provide a personalized customer experience, monitor new trends, and reveal upselling opportunities. To handle issues related to finding and fixing data quality, you will need to adopt tools with analytics algorithms and artificial intelligence applications. These tools will help monitor and fix any data quality issues constantly. You can create an intelligent data identifier that can match duplicates with data variances and report the existing typos.
Evaluating and selecting big data technologies
Data management teams often require a wide range of big data technologies to handle the challenges. They have to choose various technologies in the market that can handle the complexities associated with large volumes of data. Unfortunately, the fluctuations and other changes in the data received from disparate sources will make it difficult to evaluate and select the appropriate technology.
When evaluating and selecting big data technologies, you have to consider the current and future needs for the data from batch sources to streaming. Ensure that the technologies adopted support cloud applications and third-party data services. These technologies provide your business with seamless data movement between cloud applications, hybrid cloud systems, and on-premises systems.
Keeping costs from getting out of control
One of the business’s main challenges is controlling costs and keeping them within the pre-set range. Unfortunately, there are so many enterprises that still use existing data metrics to estimate the costs of their data infrastructure. This is usually a huge mistake, especially when a customer needs to keep on shifting. Most businesses end up facing cost challenges when adjusting to meet the expanded, richer data sets.
You need to implement fine-grained controls over queries. Any department that faces challenges with the cost should raise the issue in their discussion with the data engineering teams and management. It’s the responsibility of the business executives to approve the right policies to help keep data management costs in control. If possible, you can adopt fixed resource pricing as it will help solve challenges related to rising costs.
Generating business insights
When handling large volumes of data from different sources, generating business insights will be very difficult. Technology is advancing fast, and your recently acquired technology may get outdated within a very short span. As a result, it will become very difficult to generate business insights. Unfortunately, most organizations focus on outcomes rather than the right technology to handle big data.
To generate great insights from big data, you will need to purchase big data applications that can support KPI-based reporting, make necessary predictions and offer the correct recommendations. You will also need to hire skilled professionals with great machine learning expertise to help you generate great business insights. This will greatly impact the overall data processing and increase the ROI of setting the data environment.
Governing data environments
Governing data environments have become challenging as data applications keep getting complicated due to advancing technology. You need to train your data handling teams more often, which is not usually the case with many businesses. The challenges can even worsen when cloud architecture requires the organization to capture and store data in an unaggregated form.
You need a great data governance strategy and controls to ensure maximum security for the data being received from different sources. It’s advisable to treat data as a product and create built-in governance rules to help provide self-service access that doesn’t require oversight.
Conclusion
You need to think about how you will handle your data challenges and refine data for the right use. There is a need to democratize the data engineering process to help in collecting data from different sources and improve access to the data. If you handle most data challenges facing your business, you will get great insights in an impactful way.